Spatial analysis of underserved areas for emergency medical services assessed out-of-hospital cardiac arrest in Jeju

Geographic information systems are useful tools for analyzing and explaining the changing spatial structures of medical services. This study identified underserved areas for out-ofhospital cardiac arrest (OHCA) using a geospatial methodology and provided a visual map of the spatial imbalances of OHCA. This study used geocoding of OHCA locations, emergency medical service (EMS) incidents, and administrative districts to demonstrate a technique for identifying underserved areas for EMS-assessed OHCA. We created a 1 km hexa-grid across Jeju Island and calculated the OHCA ratio (OHCA cases/total EMS incidents) for each grid. Underserved areas were detected through a hexagrid-based geospatial analysis (Getis-Ord Gi* statistics). A total of 366,155 EMS incidents occurred during the study period. Among these, 6,806 were EMS-assessed OHCA cases. An analysis of 1,903 overlapping grids revealed that the mean distance between grids with OHCA was 28.0±15.8 km. The mean EMS-assessed OHCA ratio (EMS-assessed OHCA cases/total EMS incidents) for each grid was 0.021±0.059. Based on the Getis-Ord Gi* OHCA ratio, 118 hot spot grids (z-score≥1.96) and 19 cold spot grids (z-score≤-1.96) were identified. This study proposed a geospatial methodology to define and identify underserved areas of EMS-assessed OHCA cases using hexa-grid-based hot- and cold-spot analysis.

Authors

Publication Details

Journal
Journal of Medicine and Life Science
Published
2025-12-09
DOI
https://doi.org/10.22730/jmls.2025.10.01.01
Primary Topic
Cardiac Arrest and Resuscitation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatial analysis of underserved areas for emergency medical services assessed out-of-hospital cardiac arrest in Jeju

Seo Young Ko, J. Kang
Journal of Medicine and Life Science
Cardiac Arrest and Resuscitation
article

Spatial analysis of underserved areas for emergency medical services assessed out-of-hospital cardiac arrest in Jeju

Seo Young Ko, J. Kang
article en

Abstract

Geographic information systems are useful tools for analyzing and explaining the changing spatial structures of medical services. This study identified underserved areas for out-ofhospital cardiac arrest (OHCA) using a geospatial methodology and provided a visual map of the spatial imbalances of OHCA. This study used geocoding of OHCA locations, emergency medical service (EMS) incidents, and administrative districts to demonstrate a technique for identifying underserved areas for EMS-assessed OHCA. We created a 1 km hexa-grid across Jeju Island and calculated the OHCA ratio (OHCA cases/total EMS incidents) for each grid. Underserved areas were detected through a hexagrid-based geospatial analysis (Getis-Ord Gi* statistics). A total of 366,155 EMS incidents occurred during the study period. Among these, 6,806 were EMS-assessed OHCA cases. An analysis of 1,903 overlapping grids revealed that the mean distance between grids with OHCA was 28.0±15.8 km. The mean EMS-assessed OHCA ratio (EMS-assessed OHCA cases/total EMS incidents) for each grid was 0.021±0.059. Based on the Getis-Ord Gi* OHCA ratio, 118 hot spot grids (z-score≥1.96) and 19 cold spot grids (z-score≤-1.96) were identified. This study proposed a geospatial methodology to define and identify underserved areas of EMS-assessed OHCA cases using hexa-grid-based hot- and cold-spot analysis.

Journal of Medicine and Life ScienceVol. 23(1)
Openalex Percentile: Top 45%
Cardiac Arrest and Resuscitation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.